Load packages

suppressWarnings(suppressMessages({library(tidyverse,quietly=T)
library(Seurat,quietly=T)
library(patchwork,quietly=T)
library(viridis,quietly=T)
library(scales,quietly=T)
library(treemap,quietly=T)
library(ggplot2,quietly=T)
library(ggpubr,quietly = T)
library(Rmagic, quietly = T)
library(reticulate, quietly=T)
library(magrittr, quietly=T)
library(imager, quietly=T)
library(EBImage, quietly=T)
library(STutility, quietly=T)
library(magrittr, quietly=T)
library(dplyr, quietly=T)
library(DT, quietly=T)
library(kableExtra, quietly=T)
}))

A

Fig2A: UMAP plots of complete dataset.

aNSCs = suppressMessages(FindClusters(aNSCs, resolution = 0.3,verbose=F))
Idents(aNSCs) = "seurat_clusters"

aNSC1 = WhichCells(aNSCs, idents = 1)
aNSC3 = WhichCells(aNSCs, idents = 0)
aNSC2 = WhichCells(aNSCs, idents = c(2,4))
aNSC4 = WhichCells(aNSCs, idents = 3)

aNSCs <- SetIdent(aNSCs, cells = aNSC1 , value = "aNSC1") 
aNSCs[["Full_detailed_clusters"]] = Idents(aNSCs) 

aNSCs <- SetIdent(aNSCs, cells = aNSC2 , value = "aNSC2") 
aNSCs[["Full_detailed_clusters"]] = Idents(aNSCs) 

aNSCs <- SetIdent(aNSCs, cells = aNSC3 , value = "aNSC3") 
aNSCs[["Full_detailed_clusters"]] = Idents(aNSCs) 

aNSCs <- SetIdent(aNSCs, cells = aNSC4 , value = "aNSC4") 
aNSCs[["Full_detailed_clusters"]] = Idents(aNSCs) 

Idents(same_umap_P2_P12_P22)="orig.ident"
P12 = subset(same_umap_P2_P12_P22,idents = c("sdCLBNx1","sdCLBNx2"))

DimPlot(P12, reduction="ref.umap",cols=c("#77C67D","#ADDD92","#2DA358","#9E9E23","#DEDEDE","#DEDEDE","#DEDEDE","#DEDEDE","#DEDEDE","#DEDEDE","#DEDEDE","#DEDEDE","#DEDEDE","#DEDEDE","#DEDEDE","#DEDEDE"), label = F, pt.size = 1,group.by = "Full_clusters",repel=T)

B

Fig2B: UMAP plots of aNSCs subsets.

DimPlot(aNSCs, label = TRUE, label.size = 6, pt.size = 3, group.by = "Full_detailed_clusters", cols=c("#9E9E23","#2DA358","#ADDD92","#77C67D"))+NoLegend()

D

Fig2D: Heatmap showing the top 5 differentially expressed TF/TR for the 6 aNSC3 subclusters.

aNSCs = FindClusters(aNSCs, resolution = 2,verbose=F)
Idents(aNSCs) = "seurat_clusters"
new_cluster       <- c("aNSC1","aNSC1","aNSC2","1","aNSC2","2","aNSC2","5","3","aNSC4","6","4","aNSC4","aNSC1","aNSC1")
names(new_cluster)<- levels(aNSCs)
aNSCs         <- RenameIdents(aNSCs, new_cluster)
aNSCs$seurat_clusters   = aNSCs@active.ident
Idents(aNSCs)<-"seurat_clusters"
aNSCs_subset <- subset(aNSCs, idents = c("1", "2", "3", "4", "5", "6"), invert = FALSE)
aNSCs_subset@active.ident=factor(aNSCs_subset@active.ident, levels = c("1", "2", "3", "4", "5", "6"))
DefaultAssay(aNSCs_subset)="RNA"
genes <- row.names(aNSCs_subset)
aNSCs_subset <- ScaleData(aNSCs_subset, features = genes,verbose = F)
suppressMessages(DoHeatmap(aNSCs_subset,
          features = c("Sox4",  "Hes5", "Zeb1", "Hes6",
                       "Rfx4",  "Klf6", "Olig1", "Olig2","Top2a",   
                       "Sp9", "Arx", "Dlx1", "Dlx2","Gsx2",
                        "Notch2",   "Six3", "Hes1", "Klf4", 
                        "Clu","Sox9", "Id4", "Hopx",
                       "Emx1", "Fezf2", "Pax6", "Eomes", "Neurog2"),
cells = NULL, group.by = "ident",   group.bar = TRUE,   group.colors =c("#F0EC65","#00BA38","#00C0AF","#B983FF","#00BCD8","#619CFF"), disp.min = -0.2, disp.max = 1.2, 
assay = "RNA", label = TRUE, size = 5, hjust = 0, angle = 45, raster = TRUE, draw.lines = TRUE,  lines.width = 5,
group.bar.height = 0.02, combine = TRUE, slot = "scale.data")+ scale_fill_viridis(alpha = 1, begin = 0, end = 1, direction = 1,discrete = FALSE, option = "D")) 

E

Fig2E: UMAP plot with identity of aNSC3 subclusters.

DefaultAssay(aNSCs)="RNA"
DimPlot(aNSCs, label = TRUE, label.size = 6, pt.size = 3, cols=c("#77C67D","#ADDD92","#F0EC65","#00BA38","#00C0AF","#00BCD8","#9E9E23","#619CFF","#B983FF"))+NoLegend()

Fig2E: UMAP plot illustrating feature plots of markers identified in D.

aNSCs_matrix <- t(as.matrix(aNSCs@assays$RNA@data))
aNSCs_matrix <- aNSCs_matrix[, colSums(aNSCs_matrix != 0) > 0]
aNSCs_MAGIC <- suppressMessages(suppressWarnings((magic(aNSCs_matrix,genes,verbose = F))))
aNSCs_assay <- CreateAssayObject(data = t(as.matrix(aNSCs_MAGIC$result)))
aNSCs[["magic"]] <- aNSCs_assay
DefaultAssay(aNSCs) <- "magic"
suppressMessages(suppressWarnings(FeaturePlot(aNSCs, order=T,pt.size=2,features = c("Dlx1","Dlx2","Olig1","Olig2","Gsx2","Emx1","Eomes","Neurog2"),ncol = 2)& scale_color_viridis_c(option="B")&theme_void()))

F

Fig2F: UMAP plot identifying cells expressing pallial (red) or subpallial (blue) transcripts (or both, i.e. dual cells, yellow) within activated NSCs.

Idents(aNSCs) = "Lineage"
DimPlot(aNSCs, reduction="umap",cols = c("#DEDEDE", "#ff0000","#fde725","#0000ff"), label = F, pt.size = 2,group.by = "Lineage", order=c("GABACells","DualCells","GLUCells"))+NoLegend()

G

Fig2G: UMAP plot identifying cells expressing pallial (red) or subpallial (blue) transcripts (or both, i.e. dual cells, yellow) within the entire dataset.

Idents(P12) = "Lineage"
DimPlot(P12, group.by = "Lineage", cols = c("#DEDEDE", "#fde725", "#ff0000", "#0000ff"), order = c("GABACells", "GLUCells", "DualCells"),pt.size = 1.5)

O

Fig2O: Spatial transcriptomic (Visium) showing invasion of dorsal V-SVZ territories by subpallial marker.

se.cropped=readRDS("C:/Users/raine/OneDrive/Documents/visium/merge_se_cropped.rds")
se.cropped <- suppressWarnings(suppressMessages(se.cropped %>% 
  SCTransform(verbose = F) %>%
  RunPCA(verbose=F) %>%
  RunUMAP(reduction = "pca", dims = 1:20,verbose = F)))

FeatureOverlay(se.cropped, features = "Emx1",
sampleids = 1:3,
cols = c("light grey", "mistyrose", "darkred"),
pt.size = 3,
add.alpha = TRUE,
ncol = 3, show.sb = FALSE,
pt.alpha = 0.1)

FeatureOverlay(se.cropped, features = "Tfap2c",
sampleids = 1:3,
cols = c("light grey", "mistyrose", "darkred"),
pt.size = 3,
add.alpha = TRUE,
ncol = 3, show.sb = FALSE,
pt.alpha = 0.1)

FeatureOverlay(se.cropped, features = "Six3",
sampleids = 1:3,
cols = c("light grey", "mistyrose", "darkred"),
pt.size = 3,
add.alpha = TRUE,
ncol = 3, show.sb = FALSE,
pt.alpha = 0.1)

FeatureOverlay(se.cropped, features = "Gsx2",
sampleids = 1:3,
cols = c("light grey", "mistyrose", "darkred"),
pt.size = 3,
add.alpha = TRUE,
ncol = 3, show.sb = FALSE,
pt.alpha = 0.1)

Q

Fig2Q: Analysis of cell proportion at P2, P12 and P22.

same_umap_P2_P12_P22=readRDS("C:/Users/raine/OneDrive/Documents/BioInfo/newdataset/same_umap_P2_P12_P22.rds")
Idents(same_umap_P2_P12_P22)="orig.ident"
P2 = subset(same_umap_P2_P12_P22,idents = "p2")
P22 = subset(same_umap_P2_P12_P22,idents = "p20")
P12 = subset(same_umap_P2_P12_P22,idents = c("sdCLBNx1","sdCLBNx2"))

DimPlot(P2, pt.size = 1.5, group.by = "lineage", cols = c("#999999", "#fde725", "#ff0000", "#0000ff"), order = c("GABACells", "GLUCells", "DualCells"))+NoLegend()+theme_void()

DimPlot(P12, pt.size = 1.5, group.by = "Lineage", cols = c("#999999", "#fde725", "#ff0000", "#0000ff"), order = c("GABACells", "GLUCells", "DualCells"))+NoLegend()+theme_void()

DimPlot(P22, pt.size = 1.5, group.by = "lineage", cols = c("#999999", "#fde725", "#ff0000", "#0000ff"), order = c("GABACells", "GLUCells", "DualCells"))+NoLegend()+theme_void()

R

Fig2R: Bargraph showing the proportion of pallial qNSCs, aNSCs and TAPs at P2, P12 and P22.

same_umap_P2_P12_P22=readRDS("C:/Users/raine/OneDrive/Documents/BioInfo/newdataset/same_umap_P2_P12_P22.rds")
Idents(same_umap_P2_P12_P22)<-"Lineage"
Idents(same_umap_P2_P12_P22) <- paste(Idents(same_umap_P2_P12_P22), same_umap_P2_P12_P22$lineage, sep = '_')
same_umap_P2_P12_P22 <- RenameIdents(same_umap_P2_P12_P22,c("GLUCells_NA"="GLUCells",
                                                             "GABACells_NA"="GABACells",
                                                             "NACells_NA"="NA",
                                                             "DualCells_NA"="Dual",
                                                             "NA_GABACells"="GABACells",
                                                             "NA_NACells"="NA",
                                                             "NA_GLUCells"="GLUCells",
                                                             "NA_DualCells"="Dual"))
same_umap_P2_P12_P22$Lineage <- Idents(same_umap_P2_P12_P22)
subsetGLU=subset(same_umap_P2_P12_P22,idents = "GLUCells")
Idents(subsetGLU)<-"complete_simplified_clusters"
subsetGLU= subset(subsetGLU,idents = c("P2_aNSCs","P2_qNSCs","P2_TAPs",
                                                   "P12_aNSCs","P12_qNSCs","P12_TAPs",
                                                   "P22_aNSCs","P22_qNSCs","P22_TAPs"))
Idents(subsetGLU)<-"complete_full_clusters"
subsetGLU= subset(subsetGLU,idents = c("P12_GLUergic lineage","P12_GABAergic lineage",
                                        "P2_GLUergic lineage","P2_GABAergic lineage",
                                        "P2_Ependymal cells","P22_GABAergic lineage","P22_GLUergic lineage"),invert=T)
Idents(subsetGLU)="orig.ident"
subsetGLU <- RenameIdents(subsetGLU,c("sdCLBNx1"="P12","sdCLBNx2"="P12","p2"="P2","p20"="P22"))
subsetGLU$orig.ident <- Idents(subsetGLU)
Idents(subsetGLU)<-"complete_full_clusters"
subsetGLU <- RenameIdents(subsetGLU,c("P2_aNSC2"="P2_aNSCs", "P12_aNSC2"="P12_aNSCs", "P22_aNSC2"="P22_aNSCs",
                                       "P2_aNSC1"="P2_aNSCs", "P12_aNSC1"="P12_aNSCs", "P22_aNSC1"="P22_aNSCs",
                                       "P2_aNSC3"="P2_aNSCs", "P12_aNSC3"="P12_aNSCs", "P22_aNSC3"="P22_aNSCs",
                                       "P12_aNSC4"="P12_aNSCs", "P22_aNSC4"="P22_aNSCs"))
subsetGLU$complete_full_clusters <- Idents(subsetGLU)
subsetGLU= subset(subsetGLU,idents = c("P12_qNSC2","P2_qNSC2","P22_qNSC2"),invert=T)
suppressMessages(plot.data <- subsetGLU@meta.data %>%
   select(orig.ident, cluster = complete_full_clusters) %>%
   group_by(cluster, orig.ident ) %>%
   summarise(count = n()) %>%
   mutate(clust_total = sum(count)) %>%
   mutate(clust_prop = count / clust_total) %>%
   group_by(orig.ident) %>%
   mutate(dataset_total = sum(count)) %>%
   ungroup() %>%
   mutate(dataset_prop = count / dataset_total))

plot.data = plot.data %>% filter(cluster == "P2_aNSCs" |cluster == "P2_qNSC1"| cluster == "P2_qNSC3"|cluster == "P2_TAPs"|
                                  cluster == "P12_aNSCs" |cluster == "P12_qNSC1"| cluster == "P12_qNSC3"|cluster == "P12_TAPs" |
                               cluster == "P22_aNSCs" |cluster == "P22_qNSC1"| cluster == "P22_qNSC3"|cluster == "P22_TAPs")

colorPallial <- c("P2_qNSC1"= "#5e4500","P2_qNSC3"= "#e1b100","P2_aNSCs"= "#9aa500" ,"P2_TAPs"="#3fc1b0",
          "P12_qNSC1"= "#5e4500","P12_qNSC3"= "#e1b100","P12_aNSCs"= "#9aa500" ,"P12_TAPs"="#3fc1b0",
          "P22_qNSC1"= "#5e4500","P22_qNSC3"= "#e1b100","P22_aNSCs"= "#9aa500","P22_TAPs"="#3fc1b0")

ggplot(plot.data, aes(x = orig.ident, y = dataset_prop, fill = factor(cluster,levels=c("P2_qNSC1","P2_qNSC3","P2_TAPs","P2_aNSCs",
                                                                                        "P12_qNSC1","P12_qNSC3","P12_TAPs","P12_aNSCs",
                                                                                        "P22_qNSC1","P22_qNSC3","P22_TAPs","P22_aNSCs")))) +geom_col()+scale_x_discrete("cluster", limits = c("P2","P12","P22"))+scale_fill_manual(values=colorPallial)+theme_minimal()+NoLegend()